Zobrazeno 1 - 10
of 17 451
pro vyhledávání: '"A. Rawal"'
Autor:
Wang, Fan, Zou, Zhilin, Sakla, Nicole, Partyka, Luke, Rawal, Nil, Singh, Gagandeep, Zhao, Wei, Ling, Haibin, Huang, Chuan, Prasanna, Prateek, Chen, Chao
Publikováno v:
Volume 99, 2025, 103373
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning
Externí odkaz:
http://arxiv.org/abs/2411.03464
Autor:
Li, Junxuan, Cao, Chen, Schwartz, Gabriel, Khirodkar, Rawal, Richardt, Christian, Simon, Tomas, Sheikh, Yaser, Saito, Shunsuke
We present a new approach to creating photorealistic and relightable head avatars from a phone scan with unknown illumination. The reconstructed avatars can be animated and relit in real time with the global illumination of diverse environments. Unli
Externí odkaz:
http://arxiv.org/abs/2410.24223
Understanding how humans interact with each other is key to building realistic multi-human virtual reality systems. This area remains relatively unexplored due to the lack of large-scale datasets. Recent datasets focusing on this issue mainly consist
Externí odkaz:
http://arxiv.org/abs/2410.20294
Autor:
Rawal, Kaivalya, Lakkaraju, Himabindu
This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal p
Externí odkaz:
http://arxiv.org/abs/2409.13940
Autor:
Chen, Ruirui, Jiang, Weifeng, Qin, Chengwei, Rawal, Ishaan Singh, Tan, Cheston, Choi, Dongkyu, Xiong, Bo, Ai, Bo
The rapid obsolescence of information in Large Language Models (LLMs) has driven the development of various techniques to incorporate new facts. However, existing methods for knowledge editing still face difficulties with multi-hop questions that req
Externí odkaz:
http://arxiv.org/abs/2408.15903
Autor:
Khirodkar, Rawal, Bagautdinov, Timur, Martinez, Julieta, Zhaoen, Su, James, Austin, Selednik, Peter, Anderson, Stuart, Saito, Shunsuke
We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are ex
Externí odkaz:
http://arxiv.org/abs/2408.12569
Smart autonomous agents are becoming increasingly important in various real-life applications, including robotics and autonomous vehicles. One crucial skill that these agents must possess is the ability to interact with their surrounding entities, su
Externí odkaz:
http://arxiv.org/abs/2408.04423
Large language models (LLMs) have gained significant attention due to their ability to mimic human language. Identifying texts generated by LLMs is crucial for understanding their capabilities and mitigating potential consequences. This paper analyze
Externí odkaz:
http://arxiv.org/abs/2407.12815
Training with mixed data distributions is a common and important part of creating multi-task and instruction-following models. The diversity of the data distributions and cost of joint training makes the optimization procedure extremely challenging.
Externí odkaz:
http://arxiv.org/abs/2406.15570
Autor:
Rawal, Ruchit, Saifullah, Khalid, Farré, Miquel, Basri, Ronen, Jacobs, David, Somepalli, Gowthami, Goldstein, Tom
Current datasets for long-form video understanding often fall short of providing genuine long-form comprehension challenges, as many tasks derived from these datasets can be successfully tackled by analyzing just one or a few random frames from a vid
Externí odkaz:
http://arxiv.org/abs/2405.08813